Overview

Dataset statistics

Number of variables13
Number of observations1886
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory426.8 KiB
Average record size in memory231.7 B

Variable types

Numeric11
Categorical2

Alerts

country_name has a high cardinality: 149 distinct values High cardinality
life_ladder is highly correlated with log_gdp_per_capita and 4 other fieldsHigh correlation
log_gdp_per_capita is highly correlated with life_ladder and 2 other fieldsHigh correlation
social_support is highly correlated with life_ladder and 2 other fieldsHigh correlation
healthy_life_expectancy_at_birth is highly correlated with life_ladder and 2 other fieldsHigh correlation
freedom_to_make_life_choices is highly correlated with life_ladder and 1 other fieldsHigh correlation
positive_affect is highly correlated with life_ladder and 1 other fieldsHigh correlation
life_ladder is highly correlated with log_gdp_per_capita and 4 other fieldsHigh correlation
log_gdp_per_capita is highly correlated with life_ladder and 2 other fieldsHigh correlation
social_support is highly correlated with life_ladder and 2 other fieldsHigh correlation
healthy_life_expectancy_at_birth is highly correlated with life_ladder and 2 other fieldsHigh correlation
freedom_to_make_life_choices is highly correlated with life_ladder and 1 other fieldsHigh correlation
positive_affect is highly correlated with life_ladder and 1 other fieldsHigh correlation
life_ladder is highly correlated with log_gdp_per_capita and 2 other fieldsHigh correlation
log_gdp_per_capita is highly correlated with life_ladder and 2 other fieldsHigh correlation
social_support is highly correlated with life_ladder and 1 other fieldsHigh correlation
healthy_life_expectancy_at_birth is highly correlated with life_ladder and 1 other fieldsHigh correlation
df_index is highly correlated with log_gdp_per_capita and 1 other fieldsHigh correlation
life_ladder is highly correlated with log_gdp_per_capita and 6 other fieldsHigh correlation
log_gdp_per_capita is highly correlated with df_index and 8 other fieldsHigh correlation
social_support is highly correlated with life_ladder and 5 other fieldsHigh correlation
healthy_life_expectancy_at_birth is highly correlated with life_ladder and 4 other fieldsHigh correlation
freedom_to_make_life_choices is highly correlated with life_ladder and 6 other fieldsHigh correlation
generosity is highly correlated with log_gdp_per_capita and 1 other fieldsHigh correlation
perceptions_of_corruption is highly correlated with life_ladder and 3 other fieldsHigh correlation
positive_affect is highly correlated with life_ladder and 4 other fieldsHigh correlation
negative_affect is highly correlated with regional_indicatorHigh correlation
regional_indicator is highly correlated with df_index and 9 other fieldsHigh correlation
country_name is uniformly distributed Uniform
df_index has unique values Unique
life_ladder has unique values Unique

Reproduction

Analysis started2022-06-24 16:57:16.551886
Analysis finished2022-06-24 16:57:33.728202
Duration17.18 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct1886
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean972.3303287
Minimum0
Maximum1948
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2022-06-24T13:57:33.804203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile98.25
Q1498.25
median970.5
Q31447.75
95-th percentile1853.75
Maximum1948
Range1948
Interquartile range (IQR)949.5

Descriptive statistics

Standard deviation559.4115557
Coefficient of variation (CV)0.575330769
Kurtosis-1.167925316
Mean972.3303287
Median Absolute Deviation (MAD)475
Skewness0.01065180916
Sum1833815
Variance312941.2887
MonotonicityStrictly increasing
2022-06-24T13:57:34.005205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.1%
12811
 
0.1%
12931
 
0.1%
12921
 
0.1%
12911
 
0.1%
12901
 
0.1%
12891
 
0.1%
12881
 
0.1%
12871
 
0.1%
12861
 
0.1%
Other values (1876)1876
99.5%
ValueCountFrequency (%)
01
0.1%
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
ValueCountFrequency (%)
19481
0.1%
19471
0.1%
19461
0.1%
19451
0.1%
19441
0.1%
19431
0.1%
19421
0.1%
19411
0.1%
19401
0.1%
19391
0.1%

country_name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct149
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size120.3 KiB
Zimbabwe
 
15
Germany
 
15
Lithuania
 
15
South Africa
 
15
Denmark
 
15
Other values (144)
1811 

Length

Max length25
Median length23
Mean length8.275185578
Min length4

Characters and Unicode

Total characters15607
Distinct characters52
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan

Common Values

ValueCountFrequency (%)
Zimbabwe15
 
0.8%
Germany15
 
0.8%
Lithuania15
 
0.8%
South Africa15
 
0.8%
Denmark15
 
0.8%
Dominican Republic15
 
0.8%
Ecuador15
 
0.8%
Egypt15
 
0.8%
El Salvador15
 
0.8%
Ukraine15
 
0.8%
Other values (139)1736
92.0%

Length

2022-06-24T13:57:34.175202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
united43
 
1.9%
china39
 
1.7%
south30
 
1.3%
republic27
 
1.2%
of24
 
1.1%
north20
 
0.9%
cyprus20
 
0.9%
zimbabwe15
 
0.7%
argentina15
 
0.7%
bolivia15
 
0.7%
Other values (159)2009
89.0%

Most occurring characters

ValueCountFrequency (%)
a2438
15.6%
i1374
 
8.8%
n1267
 
8.1%
e1064
 
6.8%
r912
 
5.8%
o862
 
5.5%
t566
 
3.6%
l564
 
3.6%
u482
 
3.1%
s479
 
3.1%
Other values (42)5599
35.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter12941
82.9%
Uppercase Letter2242
 
14.4%
Space Separator371
 
2.4%
Other Punctuation33
 
0.2%
Open Punctuation10
 
0.1%
Close Punctuation10
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a2438
18.8%
i1374
10.6%
n1267
9.8%
e1064
 
8.2%
r912
 
7.0%
o862
 
6.7%
t566
 
4.4%
l564
 
4.4%
u482
 
3.7%
s479
 
3.7%
Other values (16)2933
22.7%
Uppercase Letter
ValueCountFrequency (%)
S219
 
9.8%
C212
 
9.5%
M177
 
7.9%
B161
 
7.2%
A157
 
7.0%
P124
 
5.5%
N119
 
5.3%
T118
 
5.3%
I115
 
5.1%
K111
 
5.0%
Other values (12)729
32.5%
Space Separator
ValueCountFrequency (%)
371
100.0%
Other Punctuation
ValueCountFrequency (%)
.33
100.0%
Open Punctuation
ValueCountFrequency (%)
(10
100.0%
Close Punctuation
ValueCountFrequency (%)
)10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin15183
97.3%
Common424
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a2438
16.1%
i1374
 
9.0%
n1267
 
8.3%
e1064
 
7.0%
r912
 
6.0%
o862
 
5.7%
t566
 
3.7%
l564
 
3.7%
u482
 
3.2%
s479
 
3.2%
Other values (38)5175
34.1%
Common
ValueCountFrequency (%)
371
87.5%
.33
 
7.8%
(10
 
2.4%
)10
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII15607
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a2438
15.6%
i1374
 
8.8%
n1267
 
8.1%
e1064
 
6.8%
r912
 
5.8%
o862
 
5.5%
t566
 
3.6%
l564
 
3.6%
u482
 
3.1%
s479
 
3.1%
Other values (42)5599
35.9%

year
Real number (ℝ≥0)

Distinct16
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.259809
Minimum2005
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2022-06-24T13:57:34.282237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2006
Q12010
median2013
Q32017
95-th percentile2019.75
Maximum2020
Range15
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.195279242
Coefficient of variation (CV)0.002083824066
Kurtosis-1.088867037
Mean2013.259809
Median Absolute Deviation (MAD)4
Skewness-0.1444634789
Sum3797008
Variance17.60036792
MonotonicityNot monotonic
2022-06-24T13:57:34.381316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2019144
 
7.6%
2017143
 
7.6%
2018142
 
7.5%
2014138
 
7.3%
2016138
 
7.3%
2015137
 
7.3%
2011136
 
7.2%
2012135
 
7.2%
2013132
 
7.0%
2010118
 
6.3%
Other values (6)523
27.7%
ValueCountFrequency (%)
200527
 
1.4%
200687
4.6%
200799
5.2%
2008107
5.7%
2009108
5.7%
2010118
6.3%
2011136
7.2%
2012135
7.2%
2013132
7.0%
2014138
7.3%
ValueCountFrequency (%)
202095
5.0%
2019144
7.6%
2018142
7.5%
2017143
7.6%
2016138
7.3%
2015137
7.3%
2014138
7.3%
2013132
7.0%
2012135
7.2%
2011136
7.2%

life_ladder
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct1886
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.487640503
Minimum2.375091791
Maximum8.01893425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2022-06-24T13:57:34.510362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.375091791
5-th percentile3.726088405
Q14.660373926
median5.404919863
Q36.299232125
95-th percentile7.395317435
Maximum8.01893425
Range5.643842459
Interquartile range (IQR)1.638858199

Descriptive statistics

Standard deviation1.11035049
Coefficient of variation (CV)0.2023365943
Kurtosis-0.703269227
Mean5.487640503
Median Absolute Deviation (MAD)0.8004417419
Skewness0.073335838
Sum10349.68999
Variance1.232878211
MonotonicityNot monotonic
2022-06-24T13:57:34.636325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.7235898971
 
0.1%
5.5668025021
 
0.1%
5.2038259511
 
0.1%
5.1861906051
 
0.1%
4.6396474841
 
0.1%
4.8981800081
 
0.1%
4.1802020071
 
0.1%
4.4280219081
 
0.1%
4.4935984611
 
0.1%
5.46661521
 
0.1%
Other values (1876)1876
99.5%
ValueCountFrequency (%)
2.3750917911
0.1%
2.661718131
0.1%
2.6935231691
0.1%
2.6943032741
0.1%
2.7015912531
0.1%
2.8078551291
0.1%
2.838958741
0.1%
2.902734281
0.1%
2.9045350551
0.1%
2.9362208841
0.1%
ValueCountFrequency (%)
8.018934251
0.1%
7.9708919531
0.1%
7.8893499371
0.1%
7.858107091
0.1%
7.8342332841
0.1%
7.7882518771
0.1%
7.788231851
0.1%
7.7803478241
0.1%
7.7762088781
0.1%
7.7705154421
0.1%

log_gdp_per_capita
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1862
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.388737732
Minimum6.635322094
Maximum11.64816856
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2022-06-24T13:57:34.770329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6.635322094
5-th percentile7.408512115
Q18.488996744
median9.461041451
Q310.35551786
95-th percentile10.92353702
Maximum11.64816856
Range5.01284647
Interquartile range (IQR)1.86652112

Descriptive statistics

Standard deviation1.132724906
Coefficient of variation (CV)0.1206471986
Kurtosis-0.842710587
Mean9.388737732
Median Absolute Deviation (MAD)0.933139801
Skewness-0.3246212692
Sum17707.15936
Variance1.283065712
MonotonicityNot monotonic
2022-06-24T13:57:34.958324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.46032333425
 
1.3%
7.3700995451
 
0.1%
8.5035676961
 
0.1%
9.5184497831
 
0.1%
9.4961633681
 
0.1%
9.4639501571
 
0.1%
9.4160156251
 
0.1%
8.4842033391
 
0.1%
8.5439319611
 
0.1%
8.5477371221
 
0.1%
Other values (1852)1852
98.2%
ValueCountFrequency (%)
6.6353220941
0.1%
6.6782274251
0.1%
6.7187623981
0.1%
6.7233085631
0.1%
6.741916181
0.1%
6.7481760981
0.1%
6.7758231161
0.1%
6.7869830131
0.1%
6.8225436211
0.1%
6.8382635121
0.1%
ValueCountFrequency (%)
11.648168561
0.1%
11.644917491
0.1%
11.640029911
0.1%
11.633571621
0.1%
11.616852761
0.1%
11.598289491
0.1%
11.594555851
0.1%
11.591707231
0.1%
11.579789161
0.1%
11.567008971
0.1%

social_support
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1878
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8150725741
Minimum0.2909338176
Maximum0.9873434901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2022-06-24T13:57:35.090329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.2909338176
5-th percentile0.5776550919
Q10.7526799887
median0.836037308
Q30.9061918557
95-th percentile0.9514159113
Maximum0.9873434901
Range0.6964096725
Interquartile range (IQR)0.1535118669

Descriptive statistics

Standard deviation0.1159834211
Coefficient of variation (CV)0.1422982748
Kurtosis1.079518402
Mean0.8150725741
Median Absolute Deviation (MAD)0.07455509901
Skewness-1.084311493
Sum1537.226875
Variance0.01345215398
MonotonicityNot monotonic
2022-06-24T13:57:35.229346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.83516663319
 
0.5%
0.45066231491
 
0.1%
0.8712122441
 
0.1%
0.79299813511
 
0.1%
0.83225375411
 
0.1%
0.79830503461
 
0.1%
0.78430008891
 
0.1%
0.68685477971
 
0.1%
0.73443096881
 
0.1%
0.81053793431
 
0.1%
Other values (1868)1868
99.0%
ValueCountFrequency (%)
0.29093381761
0.1%
0.29133367541
0.1%
0.3029550911
0.1%
0.32569253441
0.1%
0.3729078771
0.1%
0.38237351181
0.1%
0.41997286681
0.1%
0.42224001881
0.1%
0.43438851831
0.1%
0.43541356921
0.1%
ValueCountFrequency (%)
0.98734349011
0.1%
0.9849400521
0.1%
0.98448896411
0.1%
0.98328608271
0.1%
0.98252171281
0.1%
0.98182457691
0.1%
0.98150175811
0.1%
0.98028320071
0.1%
0.97896528241
0.1%
0.97883975511
0.1%

healthy_life_expectancy_at_birth
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct803
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.63339126
Minimum32.29999924
Maximum77.09999847
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2022-06-24T13:57:35.372360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum32.29999924
5-th percentile49.15499973
Q159.31499958
median65.19999695
Q368.59999847
95-th percentile73
Maximum77.09999847
Range44.79999924
Interquartile range (IQR)9.284998894

Descriptive statistics

Standard deviation7.319109235
Coefficient of variation (CV)0.1150199461
Kurtosis0.1476745631
Mean63.63339126
Median Absolute Deviation (MAD)4.5
Skewness-0.8080888596
Sum120012.5759
Variance53.56936
MonotonicityNot monotonic
2022-06-24T13:57:35.498358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65.1999969562
 
3.3%
72.1999969515
 
0.8%
66.4000015314
 
0.7%
67.1999969514
 
0.7%
7314
 
0.7%
66.5999984713
 
0.7%
66.8000030512
 
0.6%
72.4000015312
 
0.6%
68.0999984711
 
0.6%
65.511
 
0.6%
Other values (793)1708
90.6%
ValueCountFrequency (%)
32.299999241
0.1%
36.860000611
0.1%
40.299999241
0.1%
40.380001071
0.1%
40.808292391
0.1%
41.200000761
0.1%
41.419998171
0.1%
41.580001831
0.1%
42.099998471
0.1%
42.860000611
0.1%
ValueCountFrequency (%)
77.099998471
0.1%
76.800003051
0.1%
76.51
0.1%
76.199996951
0.1%
75.900001531
0.1%
75.680000311
0.1%
75.459999081
0.1%
75.199996951
0.1%
75.099998471
0.1%
75.019996641
0.1%

freedom_to_make_life_choices
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1855
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7450951849
Minimum0.2575338185
Maximum0.9851777554
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2022-06-24T13:57:35.630326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.2575338185
5-th percentile0.4848916233
Q10.6524778157
median0.7634760141
Q30.8551803529
95-th percentile0.9351277202
Maximum0.9851777554
Range0.7276439369
Interquartile range (IQR)0.2027025372

Descriptive statistics

Standard deviation0.1395204273
Coefficient of variation (CV)0.1872518171
Kurtosis-0.05145329041
Mean0.7450951849
Median Absolute Deviation (MAD)0.1015605927
Skewness-0.6362118413
Sum1405.249519
Variance0.01946594964
MonotonicityNot monotonic
2022-06-24T13:57:35.958362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.763476014131
 
1.6%
0.683557572
 
0.1%
0.69256764651
 
0.1%
0.64095264671
 
0.1%
0.6130557061
 
0.1%
0.60746324061
 
0.1%
0.51318395141
 
0.1%
0.55217373371
 
0.1%
0.43939977881
 
0.1%
0.7927346231
 
0.1%
Other values (1845)1845
97.8%
ValueCountFrequency (%)
0.25753381851
0.1%
0.26006931071
0.1%
0.28681439161
0.1%
0.29461178181
0.1%
0.30354040861
0.1%
0.30613189941
0.1%
0.31456461551
0.1%
0.33243611451
0.1%
0.33331209421
0.1%
0.3352236451
0.1%
ValueCountFrequency (%)
0.98517775541
0.1%
0.98380303381
0.1%
0.97993713621
0.1%
0.97113502031
0.1%
0.97029453521
0.1%
0.96989798551
0.1%
0.96978837251
0.1%
0.96858048441
0.1%
0.96456110481
0.1%
0.96439540391
0.1%

generosity
Real number (ℝ)

HIGH CORRELATION

Distinct1811
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.002257250651
Minimum-0.3350402415
Maximum0.6980987787
Zeros0
Zeros (%)0.0%
Negative1111
Negative (%)58.9%
Memory size14.9 KiB
2022-06-24T13:57:36.101360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.3350402415
5-th percentile-0.2275895476
Q1-0.1110495012
median-0.02539299708
Q30.0848489441
95-th percentile0.3018741235
Maximum0.6980987787
Range1.03313902
Interquartile range (IQR)0.1958984453

Descriptive statistics

Standard deviation0.1599232784
Coefficient of variation (CV)-70.84870188
Kurtosis1.022810449
Mean-0.002257250651
Median Absolute Deviation (MAD)0.09732069448
Skewness0.8555562826
Sum-4.257174728
Variance0.02557545499
MonotonicityNot monotonic
2022-06-24T13:57:36.235657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0253929970876
 
4.0%
0.034639194611
 
0.1%
-0.084413997831
 
0.1%
-0.087268345061
 
0.1%
-0.058499272911
 
0.1%
-0.04202008621
 
0.1%
0.079751841721
 
0.1%
0.099404059351
 
0.1%
0.032284621151
 
0.1%
-0.01015393251
 
0.1%
Other values (1801)1801
95.5%
ValueCountFrequency (%)
-0.33504024151
0.1%
-0.31643930081
0.1%
-0.30656155941
0.1%
-0.30501219631
0.1%
-0.30490773921
0.1%
-0.3032038511
0.1%
-0.30287697911
0.1%
-0.29636645321
0.1%
-0.29509571191
0.1%
-0.29305177931
0.1%
ValueCountFrequency (%)
0.69809877871
0.1%
0.68931800131
0.1%
0.68756020071
0.1%
0.67942637211
0.1%
0.65000909571
0.1%
0.64497506621
0.1%
0.56113839151
0.1%
0.55534803871
0.1%
0.55252140761
0.1%
0.53598546981
0.1%

perceptions_of_corruption
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1783
Distinct (%)94.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7508450363
Minimum0.03519798815
Maximum0.9832760096
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2022-06-24T13:57:36.375702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.03519798815
5-th percentile0.3214758933
Q10.6991195679
median0.8024281263
Q30.8680177927
95-th percentile0.9413023889
Maximum0.9832760096
Range0.9480780214
Interquartile range (IQR)0.1688982248

Descriptive statistics

Standard deviation0.1825273649
Coefficient of variation (CV)0.24309592
Kurtosis2.205344011
Mean0.7508450363
Median Absolute Deviation (MAD)0.07963296771
Skewness-1.588217771
Sum1416.093739
Variance0.03331623893
MonotonicityNot monotonic
2022-06-24T13:57:36.510672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8024281263104
 
5.5%
0.54909336571
 
0.1%
0.67019140721
 
0.1%
0.65918028351
 
0.1%
0.69222128391
 
0.1%
0.71535617111
 
0.1%
0.85472965241
 
0.1%
0.91277444361
 
0.1%
0.87314027551
 
0.1%
0.86560267211
 
0.1%
Other values (1773)1773
94.0%
ValueCountFrequency (%)
0.035197988151
0.1%
0.047311153261
0.1%
0.060282066461
0.1%
0.063614882531
0.1%
0.065775275231
0.1%
0.069619603461
0.1%
0.078000180421
0.1%
0.081324897711
0.1%
0.094604469841
0.1%
0.096562929451
0.1%
ValueCountFrequency (%)
0.98327600961
0.1%
0.98293089871
0.1%
0.9788001181
0.1%
0.97691738611
0.1%
0.9767774941
0.1%
0.97633963821
0.1%
0.97606104611
0.1%
0.97368633751
0.1%
0.97273898121
0.1%
0.97266858821
0.1%

positive_affect
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1868
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7115202186
Minimum0.3216897547
Maximum0.9436206222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2022-06-24T13:57:36.649702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.3216897547
5-th percentile0.5314744413
Q10.629254818
median0.7223914266
Q30.7992981076
95-th percentile0.8620871753
Maximum0.9436206222
Range0.6219308674
Interquartile range (IQR)0.1700432897

Descriptive statistics

Standard deviation0.1056898035
Coefficient of variation (CV)0.1485408296
Kurtosis-0.568526399
Mean0.7115202186
Median Absolute Deviation (MAD)0.08336412907
Skewness-0.3736491141
Sum1341.927132
Variance0.01117033457
MonotonicityNot monotonic
2022-06-24T13:57:36.783701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.722391426619
 
1.0%
0.51763719321
 
0.1%
0.63873720171
 
0.1%
0.63732820751
 
0.1%
0.57791155581
 
0.1%
0.64188683031
 
0.1%
0.58833652731
 
0.1%
0.56694424151
 
0.1%
0.57555246351
 
0.1%
0.60294610261
 
0.1%
Other values (1858)1858
98.5%
ValueCountFrequency (%)
0.32168975471
0.1%
0.35138705371
0.1%
0.36249768731
0.1%
0.38429245351
0.1%
0.42096188661
0.1%
0.42222747211
0.1%
0.42292764781
0.1%
0.42412531381
0.1%
0.42664796111
0.1%
0.43311527371
0.1%
ValueCountFrequency (%)
0.94362062221
0.1%
0.93437367681
0.1%
0.92456096411
0.1%
0.91893708711
0.1%
0.91680097581
0.1%
0.91049695011
0.1%
0.90277212861
0.1%
0.90126794581
0.1%
0.89981234071
0.1%
0.89971846341
0.1%

negative_affect
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1873
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2674211945
Minimum0.08273695409
Maximum0.5905387402
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2022-06-24T13:57:36.918670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.08273695409
5-th percentile0.1513236538
Q10.206374187
median0.2581173182
Q30.3189800903
95-th percentile0.4160163626
Maximum0.5905387402
Range0.5078017861
Interquartile range (IQR)0.1126059033

Descriptive statistics

Standard deviation0.08267082686
Coefficient of variation (CV)0.3091408929
Kurtosis0.30598214
Mean0.2674211945
Median Absolute Deviation (MAD)0.05472916365
Skewness0.6151876361
Sum504.3563729
Variance0.006834465613
MonotonicityNot monotonic
2022-06-24T13:57:37.069669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.258117318214
 
0.7%
0.25819548961
 
0.1%
0.29229468111
 
0.1%
0.30699834231
 
0.1%
0.33087566491
 
0.1%
0.42175191641
 
0.1%
0.36275061961
 
0.1%
0.31381905081
 
0.1%
0.37005382781
 
0.1%
0.25112289191
 
0.1%
Other values (1863)1863
98.8%
ValueCountFrequency (%)
0.082736954091
0.1%
0.092695645991
0.1%
0.093412384391
0.1%
0.094316124921
0.1%
0.095490492881
0.1%
0.099630385641
0.1%
0.10349379481
0.1%
0.10615817461
0.1%
0.10687077791
0.1%
0.1083054171
0.1%
ValueCountFrequency (%)
0.59053874021
0.1%
0.58126693961
0.1%
0.56975805761
0.1%
0.56363111731
0.1%
0.55709868671
0.1%
0.55427873131
0.1%
0.55183970931
0.1%
0.5438362361
0.1%
0.53824543951
0.1%
0.53153890371
0.1%

regional_indicator
Categorical

HIGH CORRELATION

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size144.5 KiB
Sub-Saharan Africa
390 
Latin America and Caribbean
279 
Western Europe
271 
Central and Eastern Europe
225 
Middle East and North Africa
211 
Other values (5)
510 

Length

Max length34
Median length27
Mean length21.37062566
Min length9

Characters and Unicode

Total characters40305
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth Asia
2nd rowSouth Asia
3rd rowSouth Asia
4th rowSouth Asia
5th rowSouth Asia

Common Values

ValueCountFrequency (%)
Sub-Saharan Africa390
20.7%
Latin America and Caribbean279
14.8%
Western Europe271
14.4%
Central and Eastern Europe225
11.9%
Middle East and North Africa211
11.2%
Commonwealth of Independent States170
9.0%
Southeast Asia116
 
6.2%
South Asia84
 
4.5%
East Asia82
 
4.3%
North America and ANZ58
 
3.1%

Length

2022-06-24T13:57:37.206703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-24T13:57:37.361639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
and773
13.2%
africa601
 
10.2%
europe496
 
8.5%
sub-saharan390
 
6.6%
america337
 
5.7%
east293
 
5.0%
asia282
 
4.8%
latin279
 
4.8%
caribbean279
 
4.8%
western271
 
4.6%
Other values (11)1868
31.8%

Most occurring characters

ValueCountFrequency (%)
a5199
12.9%
3983
 
9.9%
e3281
 
8.1%
n3122
 
7.7%
r3093
 
7.7%
t2558
 
6.3%
i1989
 
4.9%
d1535
 
3.8%
o1475
 
3.7%
s1357
 
3.4%
Other values (20)12713
31.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter30500
75.7%
Uppercase Letter5432
 
13.5%
Space Separator3983
 
9.9%
Dash Punctuation390
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a5199
17.0%
e3281
10.8%
n3122
10.2%
r3093
10.1%
t2558
8.4%
i1989
 
6.5%
d1535
 
5.0%
o1475
 
4.8%
s1357
 
4.4%
u1086
 
3.6%
Other values (8)5805
19.0%
Uppercase Letter
ValueCountFrequency (%)
A1278
23.5%
S1150
21.2%
E1014
18.7%
C674
12.4%
N327
 
6.0%
L279
 
5.1%
W271
 
5.0%
M211
 
3.9%
I170
 
3.1%
Z58
 
1.1%
Space Separator
ValueCountFrequency (%)
3983
100.0%
Dash Punctuation
ValueCountFrequency (%)
-390
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin35932
89.2%
Common4373
 
10.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a5199
14.5%
e3281
 
9.1%
n3122
 
8.7%
r3093
 
8.6%
t2558
 
7.1%
i1989
 
5.5%
d1535
 
4.3%
o1475
 
4.1%
s1357
 
3.8%
A1278
 
3.6%
Other values (18)11045
30.7%
Common
ValueCountFrequency (%)
3983
91.1%
-390
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII40305
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a5199
12.9%
3983
 
9.9%
e3281
 
8.1%
n3122
 
7.7%
r3093
 
7.7%
t2558
 
6.3%
i1989
 
4.9%
d1535
 
3.8%
o1475
 
3.7%
s1357
 
3.4%
Other values (20)12713
31.5%

Interactions

2022-06-24T13:57:31.866203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:17.137885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:18.520617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:20.330677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:21.737421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:23.119387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:24.722387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:26.083236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:27.582202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:28.964207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:30.439200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:31.993203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:17.276895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:18.648619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:20.450621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:21.860389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:23.247393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:24.887387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:26.221238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:27.705205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:29.087228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:30.564236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:32.115237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:17.407921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:18.778624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:20.588655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:22.027390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:23.378388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:25.008388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:26.351237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:27.825202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:29.216238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:30.694203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:32.243206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:17.525621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:18.939622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:20.713623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:22.153386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:23.508386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:25.174387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:26.485204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:27.940202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:29.330205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:30.811236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:32.389206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:17.653652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:19.059621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:20.838621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:22.299390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:23.624387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:25.282386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:26.629206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:28.060206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:29.444225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:30.927207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:32.550203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:17.780620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:19.188625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:20.991621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:22.429390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:23.755387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:25.402387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:26.787200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:28.249200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:29.571237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:31.056229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:32.675206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:17.896618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:19.301621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:21.120625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:22.540388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:23.869386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:25.514384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:26.926202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:28.357208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:29.680236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:31.250206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:32.816202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:18.030641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:19.432621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:21.253667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:22.661392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:24.001385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:25.638390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:27.072202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:28.489202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:29.805237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:31.382203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:32.928239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:18.145654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:19.550652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:21.377617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:22.771387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:24.180389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:25.748202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:27.197206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:28.602205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:29.920238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:31.497206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:33.043203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:18.269617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:19.673652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:21.495678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:22.887387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:24.326388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:25.860237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:27.318203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:28.732238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:30.204232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:31.620205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:33.172204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:18.396619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:20.206661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:21.621387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:23.006387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:24.452387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:25.973201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:27.450201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:28.847203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:30.321227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-24T13:57:31.748242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-06-24T13:57:37.522641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-24T13:57:37.719677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-24T13:57:37.913642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-24T13:57:38.117640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-24T13:57:33.367239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-24T13:57:33.633206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcountry_nameyearlife_ladderlog_gdp_per_capitasocial_supporthealthy_life_expectancy_at_birthfreedom_to_make_life_choicesgenerosityperceptions_of_corruptionpositive_affectnegative_affectregional_indicator
00Afghanistan20083.7235907.3701000.45066250.7999990.7181140.1676400.8816860.5176370.258195South Asia
11Afghanistan20094.4017787.5399720.55230851.2000010.6788960.1900990.8500350.5839260.237092South Asia
22Afghanistan20104.7583817.6467090.53907551.5999980.6001270.1205900.7067660.6182650.275324South Asia
33Afghanistan20113.8317197.6195320.52110451.9199980.4959010.1624270.7311090.6113870.267175South Asia
44Afghanistan20123.7829387.7054790.52063752.2400020.5309350.2360320.7756200.7103850.267919South Asia
55Afghanistan20133.5721007.7250290.48355252.5600010.5779550.0611480.8232040.6205850.273328South Asia
66Afghanistan20143.1308967.7183540.52556852.8800010.5085140.1040130.8712420.5316910.374861South Asia
77Afghanistan20153.9828557.7019920.52859753.2000010.3889280.0798640.8806380.5535530.339276South Asia
88Afghanistan20164.2201697.6965600.55907253.0000000.5225660.0422650.7932460.5649530.348332South Asia
99Afghanistan20172.6617187.6973810.49088052.7999990.427011-0.1213030.9543930.4963490.371326South Asia

Last rows

df_indexcountry_nameyearlife_ladderlog_gdp_per_capitasocial_supporthealthy_life_expectancy_at_birthfreedom_to_make_life_choicesgenerosityperceptions_of_corruptionpositive_affectnegative_affectregional_indicator
18761939Zimbabwe20114.8456427.8463080.86469448.1199990.632978-0.0878760.8298000.7811890.210544Sub-Saharan Africa
18771940Zimbabwe20124.9551017.9834680.89647649.5400010.469531-0.1025050.8586910.6692790.177311Sub-Saharan Africa
18781941Zimbabwe20134.6901887.9853910.79927450.9599990.575884-0.1041010.8309370.7118850.182288Sub-Saharan Africa
18791942Zimbabwe20144.1844517.9913350.76583952.3800010.642034-0.0738800.8202170.7252140.239111Sub-Saharan Africa
18801943Zimbabwe20153.7031917.9923390.73580053.7999990.667193-0.1231710.8104570.7150790.178861Sub-Saharan Africa
18811944Zimbabwe20163.7354007.9843720.76842554.4000020.732971-0.0946340.7236120.7376360.208555Sub-Saharan Africa
18821945Zimbabwe20173.6383008.0157380.75414755.0000000.752826-0.0976450.7512080.8064280.224051Sub-Saharan Africa
18831946Zimbabwe20183.6164808.0487980.77538855.5999980.762675-0.0684270.8442090.7101190.211726Sub-Saharan Africa
18841947Zimbabwe20192.6935237.9501320.75916256.2000010.631908-0.0637910.8306520.7160040.235354Sub-Saharan Africa
18851948Zimbabwe20203.1598027.8287570.71724356.7999990.643303-0.0086960.7885230.7025730.345736Sub-Saharan Africa